
Bi-linear operation is preferred for up-sampling. The same observation has also been presented in previous NAS-GAN works [1]}, [2]}.

Convolution operations with kernel size larger than three are not chosen in our searched generator. This result is consistent with the common model design paradigm.

The whole search stage is optimized under multiple objectives (IS, FID, and model size). Fig. REF  shows that there are 6 “None” operations and 3 “skip-connect” operations among 15 total normal operations. These operations greatly reduce the model size. Besides, the results in Tab. REF  show that EAGAN achieves good performance on both CIFAR-10 and STL-10 datasets, which demonstrates the effectiveness of EAGAN's multi-objective optimization.

